Estimating soil moisture at various depths from near surface ESA CCI Soil Moisture

2020 ◽  
Author(s):  
Manolis G. Grillakis ◽  
Aristeidis G. Koutroulis ◽  
Christos Polykretis ◽  
Dimitrios D. Alexakis
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
European Space Agency ◽  
Soil Layer ◽  
Global Scale ◽  
Systematic Evaluation ◽  
Multilinear Regression ◽  
Infiltration Process ◽  
Near Surface ◽  
Different Depths

<p>Soil moisture drought is a natural, reoccurring phenomenon that can affect any part of the land. It consists one of the most challenging problems for the modern agriculture as it directly affects the water, energy and food security nexus. Remote sensed soil moisture products have been proved to be valuable tools for the study of the soil moisture droughts. The European Space Agency (ESA), through the Climate Change Initiative (CCI) is currently providing nearly 4 decades of global satellite observed, fully homogenized soil moisture (SM) data for the uppermost soil layer. This data is valuable as it consists one of the most complete in time and space observed soil moisture dataset available. One of the main limitations that ESA CCI SM exhibits is the limited depth at which the soil moisture is estimated (limited to approximately 5cm of soil). In this work we use the ESA CCI SM data to estimate the Soil Water Index (SWI) at the global scale, which can serve as a soil moisture approximation for different depths. The SWI is a simple index that simulates the infiltration process. It utilizes an infiltration parameter T, which is related to the hydraulic characteristics. In this work, the T parameter is calibrated and validated at point scale based on soil moisture measurements of the International Soil Moisture Network (ISMN) and the FluxNet2015 (Tier 1) datasets. The regionalization of the T parameter at global scale is performed by linking T to physical soil descriptors using multilinear regression. Physical soil descriptors were obtained from the Soil Grids 250m dataset, i.e. bulk density, sand/silt/clay fractions, soil organic carbon and coarse fragments. The result of this operation is an SWI dataset for a series of different depths between 0 and 1m. This dataset can be used for the systematic evaluation of global hydrological models on their ability to simulate the soil water.</p>

A regionally explicit, global SWI calibration based on ISMN observations

2021 ◽  
Author(s):  
Manolis G. Grillakis
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Large Scale ◽  
Root Zone ◽  
Soil Depth ◽  
European Space Agency ◽  
Soil Layer ◽  
Added Value ◽  
Water Index ◽  
Soil Moisture Data

<p>Remote sensing has proven to be an irreplaceable tool for monitoring soil moisture. The European Space Agency (ESA), through the Climate Change Initiative (CCI), has provided one of the most substantial contributions in the soil water monitoring, with almost 4 decades of global satellite derived and homogenized soil moisture data for the uppermost soil layer. Yet, due to the inherent limitations of many of the remote sensors, only a limited soil depth can be monitored. To enable the assessment of the deeper soil layer moisture from surface remotely sensed products, the Soil Water Index (SWI) has been established as a convolutive transformation of the surface soil moisture estimation, under the assumption of uniform hydraulic conductivity and the absence of transpiration. The SWI uses a single calibration parameter, the T-value, to modify its response over time.</p><p>Here the Soil Water Index (SWI) is calibrated using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then use Artificial Neural Networks (ANNs) to find the best physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration of the T-value. The calibration is then used to assess a root zone related soil moisture for the period 2001 – 2018.</p><p>The results are compared against the European Centre for Medium-Range Weather Forecasts, ERA5 Land reanalysis soil moisture dataset, showing a good agreement, mainly over mid-latitudes. The results indicate that there is added value to the results of the machine learning calibration, comparing to the uniform T-value. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large scale soil moisture related studies.</p>

scholarly journals Root Response to Soil Water Status via Interaction of Crop Genotype and Environment

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 708
Author(s):  
Phanthasin Khanthavong ◽  
Shin Yabuta ◽  
Hidetoshi Asai ◽  
Md. Amzad Hossain ◽  
Isao Akagi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Root Distribution ◽  
Water Status ◽  
Soil Layer ◽  
Shoot Biomass ◽  
Soil Conditions ◽  
Crop Adaptation ◽  
Soil Moisture Status ◽  
Root Distributions

Flooding and drought are major causes of reductions in crop productivity. Root distribution indicates crop adaptation to water stress. Therefore, we aimed to identify crop roots response based on root distribution under various soil conditions. The root distribution of four crops—maize, millet, sorghum, and rice—was evaluated under continuous soil waterlogging (CSW), moderate soil moisture (MSM), and gradual soil drying (GSD) conditions. Roots extended largely to the shallow soil layer in CSW and grew longer to the deeper soil layer in GSD in maize and sorghum. GSD tended to promote the root and shoot biomass across soil moisture status regardless of the crop species. The change of specific root density in rice and millet was small compared with maize and sorghum between different soil moisture statuses. Crop response in shoot and root biomass to various soil moisture status was highest in maize and lowest in rice among the tested crops as per the regression coefficient. Thus, we describe different root distributions associated with crop plasticity, which signify root spread changes, depending on soil water conditions in different crop genotypes as well as root distributions that vary depending on crop adaptation from anaerobic to aerobic conditions.

scholarly journals Summer temperature response to extreme soil water conditions in the Mediterranean transitional climate regime

2021 ◽  
Author(s):  
Stefano Materia ◽  
Constantin Ardilouze ◽  
Chloé Prodhomme ◽  
Markus G. Donat ◽  
Marianna Benassi ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Soil Water ◽  
Land Surface ◽  
Heat Waves ◽  
Climate Regime ◽  
Seasonal Forecasts ◽  
Near Surface ◽  
Surface Temperatures ◽  
The Mediterranean

AbstractLand surface and atmosphere are interlocked by the hydrological and energy cycles and the effects of soil water-air coupling can modulate near-surface temperatures. In this work, three paired experiments were designed to evaluate impacts of different soil moisture initial and boundary conditions on summer temperatures in the Mediterranean transitional climate regime region. In this area, evapotranspiration is not limited by solar radiation, rather by soil moisture, which therefore controls the boundary layer variability. Extremely dry, extremely wet and averagely humid ground conditions are imposed to two global climate models at the beginning of the warm and dry season. Then, sensitivity experiments, where atmosphere is alternatively interactive with and forced by land surface, are launched. The initial soil state largely affects summer near-surface temperatures: dry soils contribute to warm the lower atmosphere and exacerbate heat extremes, while wet terrains suppress thermal peaks, and both effects last for several months. Land-atmosphere coupling proves to be a fundamental ingredient to modulate the boundary layer state, through the partition between latent and sensible heat fluxes. In the coupled runs, early season heat waves are sustained by interactive dry soils, which respond to hot weather conditions with increased evaporative demand, resulting in longer-lasting extreme temperatures. On the other hand, when wet conditions are prescribed across the season, the occurrence of hot days is suppressed. The land surface prescribed by climatological precipitation forcing causes a temperature drop throughout the months, due to sustained evaporation of surface soil water. Results have implications for seasonal forecasts on both rain-fed and irrigated continental regions in transitional climate zones.

scholarly journals Evaluation of soil moisture in CMIP5 simulations over contiguous United States using in situ and satellite observations

2016 ◽  
Author(s):  
Shanshui Yuan ◽  
Steven M. Quiring
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Soil Column ◽  
Regional Analysis ◽  
Soil Layer ◽  
Model Performance ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Near Surface

Abstract. This study provides a comprehensive evaluation of soil moisture simulations in the Coupled Model Intercomparison Project Phase 5 (CMIP5) extended historical experiment (2003 to 2012). Soil moisture from in situ and satellite sources are used to evaluate CMIP5 simulations in the contiguous United States (CONUS). Both near-surface (0–10 cm) and soil column (0–100 cm) simulations from more than 14 CMIP5 models are evaluated during the warm season (April–September). Multi-model ensemble means and the performance of individual models are assessed at a monthly time scale. Our results indicate that CMIP5 models can reproduce the seasonal variability in soil moisture over CONUS. However, the models tend to overestimate the magnitude of both near-surface and soil-column soil moisture in the western U.S. and underestimate it in the eastern U.S. There are large variations in model performance, especially in the near-surface. There are significant regional and inter-model variations in performance. Results of a regional analysis show that in deeper soil layer, the CMIP5 soil moisture simulations tend to be most skillful in the southern U.S. Based on both the satellite-derived and in situ soil moisture, CESM1, CCSM4 and GFDL-ESM2M perform best in the 0–10 cm soil layer and CESM1, CCSM4, GFDL-ESM2M and HadGEM2-ES perform best in the 0–100 cm soil layer.

No evidence that earthworms increase soil greenhouse gas emissions (CO2 and N2O) in the presence of plants and soil-moisture fluctuations

2021 ◽  
Author(s):  
Pierre Ganault ◽  
Johanne Nahmani ◽  
Yvan Capowiez ◽  
Isabelle Bertrand ◽  
Bruno Buatois ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Greenhouse Gases ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Soil Water ◽  
Water Status ◽  
Soil Layer ◽  
Agricultural Practices ◽  
Biodiversity Loss ◽  
Gas Emissions

<p>Accelerating climate change and biodiversity loss calls for agricultural practices that can sustain productivity with lower greenhouse gas emissions while maintaining biodiversity. Biodiversity-friendly agricultural practices have been shown to increase earthworm populations, but according to a recent meta-analyses, earthworms could increase soil CO<sub>2</sub> and N<sub>2</sub>O emissions by 33 and 42%, respectively. However, to date, many studies reported idiosyncratic and inconsistent effects of earthworms on greenhouse gases, indicating that the underlying mechanisms are not fully understood. Here we report the effects of earthworms (anecic, endogeic and their combination) with or without plants on CO<sub>2</sub> and N<sub>2</sub>O emissions in the presence of soil-moisture fluctuations from a mesocosms experiment. The experimental set-up was explicitly designed to account for the engineering effect of earthworms (i.e. burrowing) and investigate the consequences on soil macroporosity, soil water dynamic, and microbial activity. We found that plants reduced N<sub>2</sub>O emissions by 19.80% and that relative to the no earthworm control, the cumulative N<sub>2</sub>O emissions were 17.04, 34.59 and 44.81% lower in the anecic, both species and endogeic species, respectively. CO<sub>2</sub> emissions were not significantly affected by the plants or earthworms but depended on the interaction between earthworms and soil water content, an interaction that was also observed for the N<sub>2</sub>O emissions. Soil porosity variables measured by X-ray tomography suggest that the earthworm effects on CO<sub>2</sub> and N<sub>2</sub>O emissions were mediated by the burrowing patterns affecting the soil aeration and water status. N<sub>2</sub>O emissions decreased with the volume occupied by macropores in the deeper soil layer, whereas CO<sub>2</sub> emissions decreased with the macropore volume in the top soil layer. This study suggests that experimental setups without plants and in containers where the earthworm soil engineering effects via burrowing and casting on soil water status are minimized may be responsible, at least in part, for the reported positive earthworm effects on greenhouse gases.</p>

scholarly journals Appraisal of SMAP Operational Soil Moisture Product from a Global Perspective

2020 ◽  
Vol 12 (12) ◽  
pp. 1977 ◽  
Author(s):  
Swati Suman ◽  
Prashant K. Srivastava ◽  
George P. Petropoulos ◽  
Dharmendra K. Pandey ◽  
Peggy E. O’Neill
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Soil Layer ◽  
Product Evaluation ◽  
Global Scale ◽  
Global Perspective ◽  
Spatiotemporal Variability ◽  
Local Networks ◽  
Independent Verification ◽  
Wide Range

Space-borne soil moisture (SM) satellite products such as those available from Soil Moisture Active Passive (SMAP) offer unique opportunities for global and frequent monitoring of SM and also to understand its spatiotemporal variability. The present study investigates the performance of the SMAP L4 SM product at selected experimental sites across four continents, namely North America, Europe, Asia and Australia. This product provides global scale SM estimates at 9 km × 9 km spatial resolution at daily intervals. For the product evaluation, co-orbital in situ SM measurements were used, acquired at 14 test sites in North America, Europe, and Australia belonging to the International Soil Moisture Network (ISMN) and local networks in India. The satellite SM estimates of up to 0–5 cm soil layer were compared against collocated ground measurements using a series of statistical scores. Overall, the best performance of the SMAP product was found in North America (RMSE = 0.05 m3/m3) followed by Australia (RMSE = 0.08 m3/m3), Asia (RMSE = 0.09 m3/m3) and Europe (RMSE = 0.14 m3/m3). Our findings provide important insights into the spatiotemporal variability of the specific operational SM product in different ecosystems and environments. This study also furnishes an independent verification of this global product, which is of international interest given its suitability for a wide range of practical and research applications.

An assessment of soil moisture in the MARINE flash flood model using in situ measurements, reanalysis and satellite derived estimates

2020 ◽  
Author(s):  
Judith Eeckman ◽  
Hélène Roux ◽  
Bertrand Bonan ◽  
Clément Albergel ◽  
Audrey Douniot
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Model Calibration ◽  
Flash Flood ◽  
Soil Column ◽  
Subsurface Flow ◽  
Soil Layer ◽  
Observation Network

<p>The representation of soil moisture is a key factor for the simulation of flash flood in the Mediterranean region. The MARINE hydrological model is a distributed model dedicaded to flash flood simulation. Recent developments of the MARINE model lead to an improvement of the subsurface flow representation : on the one hand, the transfers through the subsurface take place in a homogeneous soil column based on the volumic soil water content instead of the water height. On the other hand, the soil column is divided into two layers, which represent respectively the upper soil layer and the deep weathered rocks. The aim of this work is to assess the performances of these new representations of the subsurface flow with respect to the soil saturation dynamics during flash flood events. The performances of the model are estimated with respect to three soil moisture products: i) the gridded soil moisture product provided by the LDAS-Monde assimilation chain. LDAS-Monde is based on the ISBA-a-gs land surface model and integrates high resolution spatial remote sensing data from the Copernicus Global Land Service for vegetation through data assimilation; ii) the upper soil moisture measurements taken from the SMOSMANIA observation network ; iii) The satellite derived surface soil moisture data from Sentinel1. The case study is led over two french mediterranean catchments impacted by flash flood events over the 2017-2019 period and where one SMOSMANIA station is available. Additionnal tests for the initialisation of MARINE water content for the two soil layers are assessed. Results show first that the dynamic of the soil moisture both provided by LDAS-Monde and simulated for the upper soil layer in MARINE are locally consistent with the SMOSMANIA observations. Secondly, the use of soil water content instead of water height to describe lateral flows in MARINE is cleary more relevant with respect to both LDAS-Monde simulations and SMOSMANIA stations. The dynamic of the deep layer moisture content also appears to be consistent with the LDAS-Monde product for deeper layers. However, the bias on these values strongly rely on the calibration of the new two-layers model. The opportunity of improving the two-layers model calibration is then discussed. Finally, the impact of the soil water content initialisation is shown to be significant mainly during the flood rising, and also to be dependent on the model calibration. In conclusion, the new developments presented for the representation of subsurface flow in the MARINE model appear to enhance the soil moisture simulation during flash floods, with respect to both the LDAS-Monde product and the SMOSMANIA observation network.</p>

Soil water contents and displacements monitoring, integrated into a Hydrological-Geotechnical Model for the evaluation of large-scale susceptibility to landslides triggered by rainfalls

2020 ◽  
Author(s):  
Roberto Passalacqua ◽  
Rossella Bovolenta ◽  
Bianca Federici ◽  
Alessandro Iacopino
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Ground Level ◽  
Water Infiltration ◽  
Landslide Monitoring ◽  
Simple Relationship ◽  
Capacitive Sensors ◽  
Different Depths

<p>Soil water content is often a landslide’s trigger factor, in particular the shallow ones. Although there is no simple relationship between the water content into the soil and the hydraulic conditions of the slopes at the depths at which the landslides develop, the knowledge of the actual soil moisture is fundamental for the study of landslides, thus, it should be monitored.<br>The LAMP (LAndslide Monitoring and Predicting) system is employed in the INTERREG-ALCOTRA project called AD-VITAM. LAMP (Bovolenta et al., 2016) was yet formulated for the analysis and forecasting of landslides triggered by rain. It adopts a physically based Integrated Hydrological Geotechnical (IHG) model (Passalacqua et al., 2016) and is implemented in GIS. In this Project, the IHG model is fed by data measured using a Wireless Sensor Network (WSN), this formed by low-cost and self-sufficient sensors. The WSN may gather rainfall, temperature, surface’s displacement data (these by mass-market GNSS receivers in RTK) and, in this case, soil water content (by capacitive sensors).<br>The WaterScout SM100 capacitive sensors were lab-analyzed then, recognized as satisfactory, installed on-site together with their related equipment. These sensors connect to a “Sensor Pup”, which has four available channels; therefore, four sensors are installed at each node, at different depths from ground-level, in order to achieve a vertical soil-moisture profile and the rate of infiltration.<br>The selection of the most suitable spots for the water content soil-sensors’ installations depends on the presence of shallow soil layers and of the radio signal emission-reception’s too.<br>The sensors may be set up both in vertical or horizontal direction. In general, the vertical installation is preferable. This implies the creation of small adjacent vertical holes, each one reaching a different depth, where the sensors are singularly pushed. Alternatively, the horizontal one may be adopted, by the opening of a small trench where the sensors are manually inserted at different depths, along a quasi-vertical vertical line. The full contact between the soil and the sensors is always verified, immediately after the installation, using a directly connected FieldScout reader to any single sensor. Furthermore, it is necessary to protect the emerging cables and to avoid preferential ways for water infiltration along the wiring lines.<br>The monitoring networks, installed at the two Italian sites of Mendatica and Ceriana, are currently providing informations in real-time. The data acquired at five nodes, distributed at each of these two sites (40 sensors in total), are currently relayed on a specific web-portal by a GSM connected Retriever-Modem, marking the evolutions of soil moisture profiles at depths between 10 and 85 cm from ground level: these continuous data allow the analysis of the infiltration and evapotranspiration phenomena. Moreover, a correlation between the soil moisture contents and the local displacements is made possible. Finally, a specific calibration of the SM100 sensors’ in relation to the on-site soil types is in progress.</p>

scholarly journals Using StorAge Selection functions to quantify ecohydrological controls on the time-variant age of evapotranspiration, soil water, and recharge

2018 ◽  
Author(s):  
Aaron A. Smith ◽  
Doerthe Tetzlaff ◽  
Chris Soulsby
Keyword(s):  
Soil Moisture ◽  
Surface Water ◽  
Soil Water ◽  
Water Uptake ◽  
Root Water Uptake ◽  
Soil Evaporation ◽  
Root Uptake ◽  
Water Fluxes ◽  
Near Surface ◽  
Moisture Conditions

Abstract. Quantifying ecohydrological controls on soil water availability is essential to understand temporal variations in catchment storage. Soil water is subject to numerous time-variable fluxes (evaporation, root-uptake, and recharge), each with different water ages which in turn affect the age of water in storage. Here, we adapt StorAge Selection (SAS) function theory to investigate water flow in soils and identify soil evaporation and root-water uptake sources from depth. We use this to quantify the effects of soil-vegetation interactions on the inter-relationships between water fluxes, storage, and age. The novel modification of the SAS function framework is tested against empirical data from two contrasting soil-vegetation units in the Scottish Highlands; these are characterised by significant preferential flow, transporting younger water through the soil during high soil moisture conditions. Dominant young water fluxes, along with relatively low rainfall intensities, explain relatively stable soil water ages through time and with depth. Soil evaporation sources were more time-invariant with high preference for near-surface water, independent of soil moisture conditions, and resulting in soil evaporation water ages similar to near-surface soil waters (mean age: 50–65 days). Sources of root-water uptake were more variable: preferential near-surface water uptake occurred in wet conditions, with a deeper root-uptake source during dry soil conditions, which resulted in more variable water ages of transpiration (mean age: 56–79 days). The simple model structure provides a parsimonious means of constraining the water age of multiple fluxes from the upper part of the critical zone during time-varying conditions improving our understanding of vegetation influences on catchment scale water fluxes.

scholarly journals Required sampling-density of ground-based soil moisture and brightness temperature observations for calibration/validation of L-band satellite observations based on a virtual reality

2019 ◽  
Author(s):  
Shaoning Lv ◽  
Bernd Schalge ◽  
Pablo Saavedra Garfias ◽  
Clemens Simmer
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Confidence Level ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Microwave Emission ◽  
Near Surface ◽  
Temperature Observations ◽  
Better Than

Abstract. Microwave remote sensing is the most promising tool for monitoring global-scale near-surface soil moisture distributions. With the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions in orbit, considerable efforts are made to evaluate their soil moisture products via ground observations, forward microwave transfer simulation, and retrievals. Due to the large footprint of the satellite radiometers of about 40 km in diameter and the spatial heterogeneity of soil moisture, minimum sampling densities for soil moisture are required to challenge the targeted precision. Here we use 400 m resolution simulations with the regional terrestrial system model TerrSysMP and its coupling with the Community Microwave Emission Modelling platform (CMEM) to quantify sampling distance required for soil moisture and brightness temperature validation. Our analysis suggests that an overall sampling resolution of better than 6 km is required to validate the targeted accuracy of 0.04 cm3/cm3 (70 % confidence level) in SMOS and SMAP over typical midlatitude European regions. The minimum sampling resolution depends on the land-surface inhomogeneity and the meteorological situation, which influence the soil moisture patterns, and ranges from about 7 km to 17 km for a 70 % confidence level for a typical year. At the minimum sampling resolution for a 70 % confidence level also the accuracy of footprint-averaged brightness temperature estimates is equal or better than 15 K/10 K for H/V polarization. Estimates strongly deteriorate with sparser sampling densities, e.g., at 3/9 km with 3/5 sampling sites the confidence level of derived footprint estimates can reach about 0.5–0.6 for soil moisture which is much less than the standard 0.7 requirements for ground measurements. The representativeness of ground-based soil moisture and brightness temperature observations – and thus their required minimum sampling densities – are only weakly correlated in space and time. This study provides a basis for a better understanding of sometimes strong mismatches between derived satellite soil moisture products and ground-based measurements.

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